APACPH Poster - Bibliometric (WOS)

1 Preamble

2 Analysis

Code

pacman::p_load(tidyverse, 
               bibliometrix,
               janitor,       # data cleaning
               stringr)

bibds_pm <- convert2df(file = "23-05-24 wos search.txt",
                       dbsource = "wos", 
                       format = "plaintext")

Converting your wos collection into a bibliographic dataframe

Done!


Generating affiliation field tag AU_UN from C1:  Done!

Code

missingData(bibds_pm)

$allTags
             cols missing_counts missing_pct             status
AU             AU              0        0.00          Excellent
AF             AF              0        0.00          Excellent
CR             CR              0        0.00          Excellent
AB             AB              1        0.69               Good
AR             AR            118       81.94           Critical
BP             BP             27       18.75         Acceptable
C1             C1              0        0.00          Excellent
C3             C3              6        4.17               Good
CA             CA            141       97.92           Critical
CL             CL            143       99.31           Critical
CT             CT            143       99.31           Critical
CY             CY            143       99.31           Critical
DA             DA              0        0.00          Excellent
DE             DE             17       11.81         Acceptable
DI             DI             11        7.64               Good
DT             DT              0        0.00          Excellent
EA             EA            135       93.75           Critical
EF             EF            144      100.00 Completely missing
EI             EI             26       18.06         Acceptable
EM             EM              9        6.25               Good
EP             EP             27       18.75         Acceptable
ER             ER            144      100.00 Completely missing
FU             FU             91       63.19           Critical
FX             FX             93       64.58           Critical
GA             GA              0        0.00          Excellent
ID             ID             11        7.64               Good
IS             IS             16       11.11         Acceptable
J9             J9              0        0.00          Excellent
JI             JI              0        0.00          Excellent
LA             LA              0        0.00          Excellent
NR             NR              0        0.00          Excellent
OA             OA             76       52.78           Critical
OI             OI             46       31.94               Poor
PA             PA              0        0.00          Excellent
PD             PD             30       20.83               Poor
PG             PG              0        0.00          Excellent
PI             PI              0        0.00          Excellent
PM             PM             21       14.58         Acceptable
PT             PT              0        0.00          Excellent
PU             PU              0        0.00          Excellent
PY             PY              1        0.69               Good
RI             RI             62       43.06               Poor
RP             RP              2        1.39               Good
SC             SC              0        0.00          Excellent
SI             SI            142       98.61           Critical
SN             SN             13        9.03               Good
SO             SO              0        0.00          Excellent
SP             SP            143       99.31           Critical
SU             SU            141       97.92           Critical
TC             TC              0        0.00          Excellent
TI             TI              0        0.00          Excellent
U1             U1              0        0.00          Excellent
U2             U2              0        0.00          Excellent
UT             UT              0        0.00          Excellent
VL             VL              1        0.69               Good
WC             WC              0        0.00          Excellent
WE             WE              0        0.00          Excellent
Z9             Z9              0        0.00          Excellent
DB             DB              0        0.00          Excellent
AU_UN       AU_UN              0        0.00          Excellent
AU1_UN     AU1_UN              0        0.00          Excellent
AU_UN_NR AU_UN_NR            144      100.00 Completely missing
SR_FULL   SR_FULL              0        0.00          Excellent
SR             SR              0        0.00          Excellent

$mandatoryTags
   tag                description missing_counts missing_pct     status
1   C1                Affiliation              0        0.00  Excellent
2   AU                     Author              0        0.00  Excellent
3   CR           Cited References              0        0.00  Excellent
4   DT              Document Type              0        0.00  Excellent
5   SO                    Journal              0        0.00  Excellent
6   LA                   Language              0        0.00  Excellent
7   NR Number of Cited References              0        0.00  Excellent
8   WC         Science Categories              0        0.00  Excellent
9   TI                      Title              0        0.00  Excellent
10  TC             Total Citation              0        0.00  Excellent
11  AB                   Abstract              1        0.69       Good
12  PY           Publication Year              1        0.69       Good
13  RP       Corresponding Author              2        1.39       Good
14  DI                        DOI             11        7.64       Good
15  ID              Keywords Plus             11        7.64       Good
16  DE                   Keywords             17       11.81 Acceptable

Code

bibres <- biblioAnalysis(bibds_pm, sep = ";")

2.1 General Information

Code

bibres_summary <- summary(bibres, k = 25)

MAIN INFORMATION ABOUT DATA

 Timespan                              1996 : 2022 
 Sources (Journals, Books, etc)        91 
 Documents                             144 
 Annual Growth Rate %                  10.98 
 Document Average Age                  8.59 
 Average citations per doc             34.48 
 Average citations per year per doc    2.895 
 References                            3518 
 
DOCUMENT TYPES                     
 article                         136 
 article; early access           1 
 article; proceedings paper      1 
 review                          6 
 
DOCUMENT CONTENTS
 Keywords Plus (ID)                    259 
 Author's Keywords (DE)                240 
 
AUTHORS
 Authors                               644 
 Author Appearances                    723 
 Authors of single-authored docs       1 
 
AUTHORS COLLABORATION
 Single-authored docs                  1 
 Documents per Author                  0.224 
 Co-Authors per Doc                    5.02 
 International co-authorships %        16.67 
 

Annual Scientific Production

 Year    Articles
    1996        1
    1998        1
    1999        1
    2000        3
    2001        3
    2002        1
    2003        2
    2004        4
    2005        5
    2006        1
    2007        2
    2008        3
    2009        8
    2010        4
    2011        5
    2012        5
    2013        6
    2014        4
    2015        7
    2016        2
    2017       12
    2018        9
    2019       16
    2020        9
    2021       14
    2022       15

Annual Percentage Growth Rate 10.98 


Most Productive Authors

       Authors        Articles     Authors        Articles Fractionalized
1  GRABOWSKA-FUDALA B        4 CAMAK DJ                             1.000
2  JARACZ K                  4 JIANG Y                              1.000
3  DESROSIERS J              3 ZHU W                                1.000
4  GORNA K                   3 GRABOWSKA-FUDALA B                   0.893
5  KOZUBSKI W                3 JARACZ K                             0.893
6  MISHRE RR                 3 BLAKE H                              0.833
7  RAHMAN MM                 3 LINCOLN NB                           0.833
8  VAN DEN BOS GAM           3 DESROSIERS J                         0.783
9  VISSER-MEILY JMA          3 KOZUBSKI W                           0.750
10 AERDEN LAM                2 ASIRET GD                            0.700
11 AKOSILE CO                2 JOO H                                0.700
12 ALVARO R                  2 KAPUCU S                             0.700
13 AMR M                     2 ASANO H                              0.667
14 ASANO H                   2 HASSAN S                             0.667
15 ASIRET GD                 2 MJI G                                0.667
16 AUSILI D                  2 MORIMOTO T                           0.667
17 BLAKE H                   2 SCHREINER AS                         0.667
18 BODDE K                   2 VISAGIE S                            0.667
19 BROUWER WBF               2 VISSER-MEILY JMA                     0.667
20 CARO CC                   2 NTSIEA V                             0.583
21 CHEN YJ                   2 GORNA K                              0.560
22 CHOI-KWON S               2 CARO CC                              0.533
23 COSTA JD                  2 COSTA JD                             0.533
24 DA CRUZ DMC               2 DA CRUZ DMC                          0.533
25 DE ARAUJO TL              2 VAN DEN BOS GAM                      0.533


Top manuscripts per citations

                            Paper                                         DOI  TC TCperYear   NTC
1  MCCULLAGH E, 2005, STROKE               10.1161/01.STR.0000181755.23914.53 286     15.05 2.281
2  BHAKTA BB, 2000, J NEUROL NEUROSUR PS   10.1136/jnnp.69.2.217              236      9.83 2.199
3  ELMSTAHL S, 1996, ARCH PHYS MED REHAB   10.1016/S0003-9993(96)90164-1      215      7.68 1.000
4  RIGBY H, 2009, INT J STROKE-a           10.1111/j.1747-4949.2009.00289.x   201     13.40 3.932
5  MORIMOTO T, 2003, AGE AGEING            10.1093/ageing/32.2.218            187      8.90 1.380
6  BUGGE C, 1999, STROKE                   10.1161/01.STR.30.8.1517           183      7.32 1.000
7  THOMMESSEN B, 2002, INT J GERIATR PSYCH 10.1002/gps.524                    165      7.50 1.000
8  ZOROWITZ RD, 2013, NEUROLOGY            10.1212/WNL.0b013e3182764c86       150     13.64 3.191
9  VAN EXEL NJA, 2004, CLIN REHABIL        10.1191/0269215504cr723oa          150      7.50 1.609
10 VISSER-MEILY JMA, 2004, CLIN REHABIL    10.1191/0269215504cr776oa          123      6.15 1.319
11 HALEY WE, 2010, STROKE                  10.1161/STROKEAHA.109.568279       116      8.29 1.751
12 VAN EXEL NJA, 2005, CEREBROVASC DIS     10.1159/000081906                  112      5.89 0.893
13 CAMAK DJ, 2015, J CLIN NURS             10.1111/jocn.12884                 103     11.44 2.326
14 TOOTH L, 2005, BRAIN INJURY             10.1080/02699050500110785          100      5.26 0.797
15 CHOI-KWON S, 2005, ARCH PHYS MED REHAB  10.1016/j.apmr.2004.09.013          95      5.00 0.758
16 DENNO MS, 2013, ARCH PHYS MED REHAB     10.1016/j.apmr.2013.03.014          91      8.27 1.936
17 BLAKE H, 2003, CLIN REHABIL             10.1191/0269215503cr613oa           84      4.00 0.620
18 MCPHERSON CJ, 2010, REHABIL PSYCHOL     10.1037/a0019359                    81      5.79 1.223
19 PUCCIARELLI G, 2017, STROKE             10.1161/STROKEAHA.116.014989        80     11.43 3.333
20 KRUITHOF WJ, 2016, PATIENT EDUC COUNS   10.1016/j.pec.2016.04.007           78      9.75 1.880
21 JARACZ K, 2015, PATIENT EDUC COUNS      10.1016/j.pec.2015.04.008           69      7.67 1.558
22 HU P, 2018, MEDICINE                    10.1097/MD.0000000000012638         66     11.00 2.055
23 ILSE IB, 2008, DISABIL REHABIL          10.1080/09638280701355645           65      4.06 1.625
24 GBIRI CA, 2015, ANN PHYS REHABIL MED    10.1016/j.rehab.2014.09.017         58      6.44 1.310
25 CHUMBLER NR, 2004, INT J GERIATR PSYCH  10.1002/gps.1187                    58      2.90 0.622


Corresponding Author's Countries

          Country Articles    Freq SCP MCP MCP_Ratio
1  USA                  20 0.13986  18   2     0.100
2  CHINA                14 0.09790  11   3     0.214
3  TURKEY               10 0.06993  10   0     0.000
4  UNITED KINGDOM       10 0.06993  10   0     0.000
5  NETHERLANDS           9 0.06294   9   0     0.000
6  CANADA                7 0.04895   7   0     0.000
7  INDIA                 7 0.04895   6   1     0.143
8  IRAN                  7 0.04895   6   1     0.143
9  NIGERIA               7 0.04895   4   3     0.429
10 BRAZIL                6 0.04196   4   2     0.333
11 KOREA                 5 0.03497   2   3     0.600
12 POLAND                4 0.02797   4   0     0.000
13 SOUTH AFRICA          4 0.02797   4   0     0.000
14 JAPAN                 3 0.02098   3   0     0.000
15 NORWAY                3 0.02098   3   0     0.000
16 SWEDEN                3 0.02098   3   0     0.000
17 THAILAND              3 0.02098   2   1     0.333
18 AUSTRALIA             2 0.01399   2   0     0.000
19 EGYPT                 2 0.01399   0   2     1.000
20 ITALY                 2 0.01399   1   1     0.500
21 SINGAPORE             2 0.01399   0   2     1.000
22 SPAIN                 2 0.01399   2   0     0.000
23 UGANDA                2 0.01399   0   2     1.000
24 BELGIUM               1 0.00699   1   0     0.000
25 BENIN                 1 0.00699   1   0     0.000


SCP: Single Country Publications

MCP: Multiple Country Publications


Total Citations per Country

     Country      Total Citations Average Article Citations
1  USA                        757                      37.9
2  UNITED KINGDOM             607                      60.7
3  NETHERLANDS                549                      61.0
4  CANADA                     444                      63.4
5  DENMARK                    286                     286.0
6  JAPAN                      221                      73.7
7  CHINA                      217                      15.5
8  NORWAY                     213                      71.0
9  KOREA                      181                      36.2
10 POLAND                     140                      35.0
11 INDIA                      134                      19.1
12 NIGERIA                    131                      18.7
13 ITALY                      125                      62.5
14 BRAZIL                     113                      18.8
15 SWEDEN                     101                      33.7
16 AUSTRALIA                  100                      50.0
17 SPAIN                       89                      44.5
18 IRAN                        75                      10.7
19 BELGIUM                     65                      65.0
20 TURKEY                      60                       6.0
21 THAILAND                    37                      12.3
22 ISRAEL                      27                      27.0
23 JORDAN                      19                      19.0
24 BENIN                       12                      12.0
25 SOUTH AFRICA                12                       3.0


Most Relevant Sources

                                                      Sources        Articles
1  CEREBROVASCULAR DISEASES                                                 6
2  CLINICAL REHABILITATION                                                  6
3  STROKE                                                                   6
4  TOPICS IN STROKE REHABILITATION                                          5
5  ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION                         4
6  DISABILITY AND REHABILITATION                                            4
7  REHABILITATION NURSING                                                   4
8  ANNALS OF INDIAN ACADEMY OF NEUROLOGY                                    3
9  BMJ OPEN                                                                 3
10 INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH        3
11 INTERNATIONAL JOURNAL OF STROKE                                          3
12 JOURNAL OF NEUROSCIENCE NURSING                                          3
13 MEDICINE                                                                 3
14 SOUTH AFRICAN JOURNAL OF PHYSIOTHERAPY                                   3
15 ANNALS OF PHYSICAL AND REHABILITATION MEDICINE                           2
16 BMC PUBLIC HEALTH                                                        2
17 BRAIN INJURY                                                             2
18 DISABILITY AND HEALTH JOURNAL                                            2
19 HEALTH & SOCIAL CARE IN THE COMMUNITY                                    2
20 HEALTH AND QUALITY OF LIFE OUTCOMES                                      2
21 INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY                            2
22 INTERNATIONAL JOURNAL OF NURSING PRACTICE                                2
23 JOURNAL OF CLINICAL NURSING                                              2
24 JOURNAL OF STROKE & CEREBROVASCULAR DISEASES                             2
25 PATIENT EDUCATION AND COUNSELING                                         2


Most Relevant Keywords

    Author Keywords (DE)      Articles Keywords-Plus (ID)     Articles
1  STROKE                          100    QUALITY-OF-LIFE           49
2  CAREGIVERS                       36    SURVIVORS                 47
3  BURDEN                           30    DETERMINANTS              36
4  CAREGIVER                        29    HEALTH                    35
5  CAREGIVER BURDEN                 29    IMPACT                    31
6  DEPRESSION                       25    SPOUSES                   31
7  QUALITY OF LIFE                  24    FAMILY CAREGIVERS         30
8  REHABILITATION                   12    CARE                      28
9  ANXIETY                          10    INFORMAL CAREGIVERS       28
10 NURSING                           7    DEPRESSION                18
11 STRAIN                            7    PEOPLE                    15
12 STROKE SURVIVORS                  7    REHABILITATION            15
13 CAREGIVING                        6    SUPPORT                   15
14 FAMILY CAREGIVER                  6    CARERS                    13
15 SOCIAL SUPPORT                    6    VALIDATION                13
16 ACTIVITIES OF DAILY LIVING        5    PREVALENCE                12
17 CARE BURDEN                       5    SCALE                     12
18 STROKE SURVIVOR                   5    EXPERIENCES               11
19 ELDERLY                           4    PREDICTORS                11
20 FAMILY CAREGIVERS                 4    RISK                      11
21 FATIGUE                           4    SOCIAL SUPPORT            11
22 INFORMAL CAREGIVERS               4    STRAIN                    10
23 QUALITATIVE                       4    ANXIETY                    9
24 STRESS                            4    BURDEN                     9
25 STROKE OUTCOME                    4    RELIABILITY                9

Code

#plot(bibres, k = 25)

2.1.1 Publication per year

Code

tibble(Article = rownames(bibds_pm),
       Year = bibds_pm$PY) %>% 
  group_by(Year) %>% 
  summarise(n = n()) %>% 
  ggplot(aes(x = Year, y = n)) +
  geom_area(alpha = .2) +
  geom_point() +
  geom_line() +
  scale_y_continuous(breaks = seq(0,28,4)) +
  scale_x_continuous(breaks = seq(1987,2023,4)) +
  coord_cartesian(ylim = c(0,28)) +
  labs(x = "Year", y = "Number of Publication") +
  theme_bw()

2.2 Language

Code

bib_lang <- bibds_pm %>% 
  group_by(LA) %>% 
  summarise(n = n()) %>% 
  mutate(percent = n / sum(n) * 100,
         percent = round(percent, 1)) %>% 
  arrange(desc(n))

bib_lang

2.3 Countries

Code

bibres_countrylist <- bibres$Countries

bibres_countrytable <- tibble(Rank = seq_along(bibres_countrylist),
                              Country = rownames(bibres_countrylist),
                              Np = as.integer(bibres_countrylist))

bibres_countrytable

Code

bibressum_countrytable <- tibble(Rank = 1:25,
                                 bibres_summary$TCperCountries) %>% 
  rename("Country" = "Country     ") %>% 
  mutate(Country = str_trim(Country),
         Country = fct_reorder(Country, Rank),
         `Total Citations` = as.integer(`Total Citations`),
         `Average Article Citations` = as.double(`Average Article Citations`),
         percent = `Total Citations` / sum(`Total Citations`) * 100,
         percent = round(percent,1)) %>% 
  inner_join(x = ., y = select(bibres_countrytable, Country, Np), 
             by = "Country") %>% 
  relocate(percent, .after = `Total Citations`)

bibressum_countrytable

Code

bib_concolab <- metaTagExtraction(bibds_pm, Field = "AU_CO", sep = ";")

bib_concolab_NetMatrix <- biblioNetwork(bib_concolab, analysis = "collaboration",
                                        network = "countries", sep = ";")

bib_concolab_Plot <- networkPlot(bib_concolab_NetMatrix,
                                 n = dim(bib_concolab_NetMatrix)[1],
                                 Title = "Country collaboration",
                                 type = "auto",
                                 size=20,
                                 size.cex=T,
                                 edgesize = 2,
                                 labelsize=1,
                                 #edges.min = 1,
                                 remove.isolates = T,
                                 community.repulsion = 0,
                                 cluster = "optimal"
                                 )

for collaboration network, using biblioshiny is nicer

2.4 Institution

Code

bibres_instlist <- bibres$Affiliations

bibres_insttable <- tibble(Rank = seq_along(bibres_instlist),
                           InstitutionAffiliation = rownames(bibres_instlist),
                           Np = as.integer(bibres_instlist)) %>% 
  mutate(InstitutionAffiliation = fct_reorder(InstitutionAffiliation, Rank))

bibres_insttable

Code

bib_educolab_NetMatrix <- biblioNetwork(bibds_pm, analysis = "collaboration",
                                       network = "universities", sep = ";")

bib_educolab_Plot <- networkPlot(bib_educolab_NetMatrix, 
                                 n = 100, 
                                 cluster = "optimal", 
                                 type = "auto",
                                 size.cex = F, 
                                 size = 3, 
                                 remove.multiple = F,
                                 labelsize=1, 
                                 alpha = .7, 
                                 edgesize = 1,
                                 edges.min = 2, 
                                 remove.isolates = T, 
                                 community.repulsion = 0,
                                 Title = "Institutions collaboration")

2.5 Journal

Code

bibres_sourcelist <- bibres$Sources

bibres_sourcetable <- tibble(Rank = seq_along(bibres_sourcelist),
                             SourceJournal = rownames(bibres_sourcelist),
                             Np = as.integer(bibres_sourcelist)) %>% 
  mutate(SourceJournal = fct_reorder(SourceJournal, Rank))

bibres_sourcetable

Code

bibres_sourcetablepercent <- bibres_sourcetable %>% 
  count(Np) %>% 
  mutate(percent = n / sum(n) * 100,
         percent = round(percent,1))

bibres_sourcetablepercent

Code

bib_bradford <- bradford(bibds_pm)

bib_bradfordtable <- bib_bradford$table %>% 
  select(Zone, Freq, Rank) %>% 
  tibble() %>% 
  group_by(Zone) %>% 
  summarise(nSO = n(),
            nArt = sum(Freq),
            RankRange = str_c(min(Rank), max(Rank), sep = "-")) %>% 
  mutate(percent = nArt / sum(nArt) * 100,
         percent = round(percent,1))

bib_bradfordtable

Code

# bib_CRSO <- metaTagExtraction(bibds_pm, Field = "CR_SO", sep = ";")
# bib_CRSO_NetMatrix <- biblioNetwork(bib_CRSO, analysis = "co-citation", 
#                                     network = "sources", sep = ";")
# bib_CRSO_Plot <- networkPlot(bib_CRSO_NetMatrix, n = 20, 
#                              Title = "Co-citation Network", type = "auto", 
#                              size.cex = T, size = 20, remove.multiple = F,
#                              labelsize = 1, edgesize = 5, edges.min = 5, alpha = 1)

co-citation network not available

2.6 Author

Code

bibds_noaufreq <- bibds_pm %>% 
  select(TI, AU, DT) %>% 
  tibble() %>% 
  mutate(no_auth = str_count(AU, pattern = ";") + 1) %>% 
  rename("paper" = "TI", "author" = "AU", "type" = "DT") %>% 
  group_by(no_auth, type) %>% 
  summarise(freq = n(), .groups = "drop") %>% 
  mutate(percent = freq / sum(freq) * 100,
         percent = round(percent,1))

bibds_noaufreq %>% 
  ggplot(aes(no_auth, freq)) + 
  geom_bar(stat = "identity") + 
  labs(x = "Number of Authors", y = "Frequency (Number of Articles)") + 
  scale_x_continuous(breaks = seq(-3,30,2)) +
  scale_y_continuous(breaks = seq(-2,40,4)) +
  theme_bw()

Code

bibres_aulist <- bibres$Authors

bibres_autable <- tibble(Rank = seq_along(bibres_aulist),
                         Author = rownames(bibres_aulist),
                         Np = as.integer(bibres_aulist)) %>% 
  mutate(Author = fct_reorder(Author, Rank))

bibres_autable

Code

bibres_autablepercent <- bibres_autable %>% 
  count(Np) %>% 
  mutate(percent = n / sum(n) * 100,
         percent = round(percent,1))

bibres_autablepercent

Code

# bib_AuCoupling_NetMatrix <- biblioNetwork(bibds_pm, analysis = "coupling",
#                                           network = "authors", sep = ";")
# 
# bib_AuCoupling_Plot <- networkPlot(bib_AuCoupling_NetMatrix, n = 15, 
#                                    cluster = "optimal", type = "auto", 
#                                    size.cex = T, size = 20, remove.multiple = F,
#                                    Title = "Bibliographic coupling of the authors",
#                                    alpha = .7)

error also

2.7 Articles

Code

tibble(Title = bibds_pm$TI,
       Author = bibds_pm$AU,
       DOI = bibds_pm$DI,
       Citations = bibds_pm$TC) %>% 
  mutate(across(.cols = c(Title, Author), .fns = str_to_title)) %>% 
  arrange(desc(Citations)) %>% 
  head(n=10)

2.8 Keyword

Code

cbind(Rank = 1:25, bibres_summary$MostRelKeywords)

Code

bib_kwco_NetMatrix <- biblioNetwork(bibds_pm, analysis = "co-occurrences", 
                                     network = "keywords", sep = ";")

bib_kwco_Plot <- networkPlot(bib_kwco_NetMatrix, normalize = "association", 
                             n = 20, Title = "Keyword Co-occurences", 
                             cluster = "optimal", type = "fruchterman", 
                             size.cex = T, size = 20,  remove.multiple = F, 
                             edgesize = 7, labelsize = 3, label.cex = T, 
                             label.n = 20, edges.min = 10)

for co-occurence network, using biblioshiny is nicer

Code

bib_thememap <- thematicMap(bibds_pm, field = "DE", n = 200, minfreq = 20, 
                            stemming = F, size = .5, n.labels = 4, repel = T)

plot(bib_thememap$map)